Efficiently use Dense layers in parallel

I need to implement a layer in Tensorflow for a dataset of size N where each sample has a set of M independent features (each feature is represented by a tensor of dimension L). I want to train M dense layers in parallel, then concatenate the outputted tensors.

I could implement a layer using for loop as below:

class MyParallelDenseLayer(tf.keras.layers.Layer):
    
    def __init__(self, dense_kwargs, **kwargs):
        super().__init__(**kwargs)
        self.dense_kwargs = dense_kwargs
    
    def build(self, input_shape):
        self.N, self.M, self.L = input_shape
        list_dense_layers = [tf.keras.layers.Dense(**self.dense_kwargs) for a_m in range(self.M)]
        super().build(input_shape)
        
    def call(self, inputs):
        parallel_output = [list_dense_layers[i](inputs[:, i]) for i in range(self.M)]
        return tf.keras.layers.Concatenate()(parallel_output )

But the for loop in the 'call' function makes my layer extremely slow. Is there a faster way to do this layer?

source https://stackoverflow.com/questions/72777856/efficiently-use-dense-layers-in-parallel

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